arXiv Computer Vision
SGF+: Decoupling Gradient Flows for Autoregressive Video Generation
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Connected Self Forcing: Beyond Local Learning in Video Autoregression
The paper introduces Connected Self Forcing, a training framework that reconnects gradient paths across autoregressive video chunks to improve long‑horizon visual quality and temporal consistency. It allows feedback from later predictions to influence earlier context generation, using shortcut gradient replay to keep memory usage manageable. The method is combined with distribution‑matching distillation and shows experimental gains without altering the inference procedure.
arXiv Computer Vision
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Relax Forcing: Relaxed KV-Memory for Consistent Long Video Generation
The paper introduces Relax Forcing, a training‑free memory mechanism for autoregressive video diffusion that structures temporal context into Sink, Tail, and History frames. By selecting History frames with a relaxation criterion, the method reduces error accumulation and attention overhead while preserving motion dynamics. Experiments on VBench‑Long demonstrate that this structured memory improves long‑video generation quality over existing baselines.
arXiv AI
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Rollout-Marginal Distillation for Long-Horizon Autoregressive Video Generation
arXiv:2609.37925v1 Announce Type: cross Abstract: Autoregressive (AR) video diffusion enables low-latency, streamable video generation, but prediction errors often accumulate over long rollouts. Trai...
arXiv Computer Vision
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Learning from Next-Frame Prediction: Autoregressive Video Modeling Encodes Effective Representations
arXiv:2512.21004v2 Announce Type: replace Abstract: Recent advances in pretraining general foundation models have significantly improved performance across diverse downstream tasks. While autoregress...
arXiv AI
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In-Context Forcing: Uncovering Context Effects in Autoregressive Video Diffusion
arXiv:2608. 05237v1 Announce Type: cross Abstract: Current few-step autoregressive video diffusion models depend on previous fully denoised clean frames as context for all denoising steps of the current frame.
arXiv AI
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Prompt-Adapter Context Routing for Parameter-Efficient Multi-Shot Long Video Extrapolation
arXiv:2607. 06481v1 Announce Type: cross Abstract: We present PACR-Video, a parameter-efficient framework for multi-shot long video extrapolation that preserves recurring entities, scene structure, visual style, and causal progression without full generator fine-tuning.